Adobe, Inc.

Yaman Kumar

Balaji Krishnamurthy

Nikaash Puri

Mihir Naware

Hyman Chung

Kumar Mrityunjay Singh

Kunal Kumar Jain

Matthew William Rozen

Pranjal Prasoon

Somesh Singh

William Brandon George

Deepak Pai

Milan Aggarwal

Lauren Dest

Joshua Butikofer

Shankar Venkitachalam

Simra Shahid

Sukriti Verma

Brandon Pack

Vinh Ngoc Khuc

Vijay Srivastava

Nimish Srivastav

Mihai Corlan

Timothy Chia-chi Liu

Alexander Klimetschek

Abhinav Chauhan

Shirsh Bansal

Sandipan Karmakar

Sean Steimer

Florin Silviu Iordache

Horea Bacila

Umang Moorarka

Suman Basetty

Clinton Hansen Goudie-Nice

James Patrick O'Reilly

Sumit Bhatia

Jayakumar Subramanian

Nikitha Srikanth

Ganesh Karbhari Palwe

Nisarg Shailesh Joshi

Seoyoung Park

Nithyakala Sainath

Heril Changwal

Sohan Patnaik

Content relevance based table query answering is described. In one or more examples, a query and a table are received. The table includes a plurality of cells. A plurality of scores for calculated that correspond to the plurality of cells based on the query. One or more machine-learning models are then leveraged to generate a search result from the query, table, and scores, which is presented in a user interface for display.

Content relevance based table query answering

In implementations of systems for digital content analysis, a computing device implements an analysis system to extract a first content component and a second content component from digital content to be analyzed based on content metrics. The analysis system generates first embeddings using a first machine learning model and second embedding using a second machine learning model. The first embeddings and the second embeddings are combined as concatenated embeddings. The analysis system generates an indication of a content metric for display in a user interface using a third machine learning model based on the concatenated embeddings.

Digital content analysis

Methods, computer systems, computer-storage media, and graphical user interfaces are provided for determining user affinities by tracking historical user interactions with tagged digital content and using the user affinities in content generation applications. Accordingly, the system may track user interactions with published digital content in order to generate user interaction reports whenever a user engages with the digital content. The system may aggregate the interaction reports to generate an affinity profile for a user or audience of users. A marketer may then generate digital content for a target user or audience of users and the system may process the digital content to generate a set of tags for the digital content. Based on the set of tags, the system may then evaluate the digital content in view of the affinity profile for the target user/audience to determine similarities or differences between the digital content and the affinity profile.

Determining user affinities for content generation applications

Content creation techniques are described that leverage content analytics to provide insight and guidance as part of content creation. To do so, content features are extracted by a content analytics system from a plurality of content and used by the content analytics system as a basis to generate a content dataset. Event data is also collected by the content analytics system from an event data source. Event data describes user interaction with respective items of content, including subsequent activities in both online and physical environments. The event data is then used to generate an event dataset. An analytics user interface is then generated by the content analytics system using the content dataset and the event dataset and is usable to guide subsequent content creation and editing.

Content analytics as part of content creation

A pose-invariant visual speech recognition system obtains a single view input of a speaker, such as a single video stream captured by a single camera. The single view input provides a particular pose of the speaker, which refers to a view angle, relative to the lens or image capture component of the camera that captured the video of the speaker, at which the speaker's face is captured. The pose of the speaker is used to select a visual speech recognition model to use to generate a text label that is the words spoken by the speaker. One or more additional view angles of the speaker are also generated from the single view input of the speaker. These one or more additional view angles, along with the single view input of the speaker, are used by the selected visual speech recognition model to generate the text label for the speaker.

Pose-invariant visual speech recognition using a single view input

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Inder Puri, India

Yaman Kumar